Search results for: cleaning machine

Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

Procedia PDF Downloads 133

Authors: Waqas Ashraf, Yasir Nawab, Haritham Khan, Habib Awais, Shahbaz Ahmad

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Seersucker is a popular lightweight fabric widely used in men’s and women’s suiting, casual wear, children’s clothing, house robes, bed spreads and for spring and summer wear. The puckered effect generates air spaces between body and the fabric, keeping the wearer cool in hot conditions. The aim of this work was to develop knitted seersucker fabric on single cylinder weft knitting machine using plain jersey structure. Core spun cotton yarn and cotton spun yarn of same linear density were used. Core spun cotton yarn, contains cotton fiber in the sheath and elastase filament in the core. The both yarn were fed at regular interval to feeders on the machine. The loop length and yarn tension were kept constant at each feeder. The samples were then scoured and bleached. After wet processing, the fabric samples were washed and tumble dried. Parameters like loop length, stitch density and areal density were measured after conditioning these samples for 24 hours in Standard atmospheric condition. Produced sample has a regular puckering stripe along the width of the fabric with same height. The stitch density of both the flat and puckered area of relaxed fabric was found to be different .Air permeability and moisture management tests were performed. The results indicated that the knitted seersucker fabric has better wicking and moisture management properties as the flat area contact, whereas puckered area held away from the skin. Seersucker effect in knitted fabric was achieved by the difference of contraction of both sets of courses produced from different types of yarns. The seer sucker fabric produce by knitting technique is less expensive as compared to woven seer sucker fabric as there is no need of yarn preparation. The knitted seersucker fabric is more practicable for summer dresses, skirts, blouses, shirts, trousers and shorts.

Keywords: air permeability, knitted structure, moisture management, seersucker

Procedia PDF Downloads 317

Authors: Joseph Bertrand Iboum Kissaaka, Charles Fonyuy Ngum Tchioben, Paul Gustave Fowe Kwetche, Jeannette Ngo Elogan Ntem, Joseph Binyet Njebakal, Ribert Yvan Makosso-Tchapi, François Mvondo Owono, Marie Joseph Ntamak-Nida

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Located in the Gulf of Guinea, the Kribi-Campo sub-basin belongs to the Aptian salt basins along the West African Margin. In this paper, we investigated the tectono-stratigraphic architecture of the basin, focusing on the role of salt tectonics and strike-slip faults along the Kribi Fracture Zone with implications for reservoir prediction. Using 2D seismic data and well data interpreted through sequence stratigraphy with integrated seismic attributes analysis with Python Programming and unsupervised Machine Learning, at least six second-order sequences, indicating three main stages of tectono-stratigraphic evolution, were determined: pre-salt syn-rift, post-salt rift climax and post-rift stages. The pre-salt syn-rift stage with KTS1 tectonosequence (Barremian-Aptian) reveals a transform rifting along NE-SW transfer faults associated with N-S to NNE-SSW syn-rift longitudinal faults bounding a NW-SE half-graben filled with alluvial to lacustrine-fan delta deposits. The post-salt rift-climax stage (Lower to Upper Cretaceous) includes two second-order tectonosequences (KTS2 and KTS3) associated with the salt tectonics and Campo High uplift. During the rift-climax stage, the growth of salt diapirs developed syncline withdrawal basins filled by early forced regression, mid transgressive and late normal regressive systems tracts. The early rift climax underlines some fine-grained hangingwall fans or delta deposits and coarse-grained fans from the footwall of fault scarps. The post-rift stage (Paleogene to Neogene) contains at least three main tectonosequences KTS4, KTS5 and KTS6-7. The first one developed some turbiditic lobe complexes considered as mass transport complexes and feeder channel-lobe complexes cutting the unstable shelf edge of the Campo High. The last two developed submarine Channel Complexes associated with lobes towards the southern part and braided delta to tidal channels towards the northern part of the Kribi-Campo sub-basin. The reservoir distribution in the Kribi-Campo sub-basin reveals some channels, fan lobes reservoirs and stacked channels reaching up to the polygonal fault systems.

Keywords: tectono-stratigraphic architecture, Kribi-Campo sub-basin, machine learning, pre-salt sequences, post-salt sequences

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Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

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Authors: Marcin Baranski

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This article presents a vibration diagnostic method designed for permanent magnets (PM) traction motors. Those machines are commonly used in traction drives of electrical vehicles. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. This work presents: field-circuit model, results of static tests, results of calculations and simulations.

Keywords: electrical vehicle, permanent magnet, traction drive, vibrations, electrical machine, eccentricity

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Authors: Okba Belahssen, Said Benramache

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This paper presents wear behavior of the plasma-nitrided 20MnCr5 steel. Untreated and plasma nitrided samples were tested. The morphology was observed by scanning electron microscopy (SEM). The plasma nitriding behaviors of 20MnCr5 steel have been assessed by evaluating tribological properties and surface hardness by using a pin-on-disk wear machine and microhardness tester. Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer improve the wear resistance.

Keywords: plasma-nitriding, alloy 20mncr5, steel, friction, wear

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Authors: Driouiche Ali, Karmal Ilham

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The Agadir region is characterized by a dry climate, ranging from arid attenuated by oceanic influences to hyper-arid. The water mobilized in the agricultural sector of greater Agadir is 95% of underground origin and comes from the water table of Chtouka. The rest represents the surface waters of the Youssef Ben Tachfine dam. These waters are intended for the irrigation of 26880 hectares of modern agriculture. More than 120 boreholes and wells are currently exploited. Their depth varies between 10 m and 200 m and the unit flow rates of the boreholes are 5 to 50 l/s. A drop in the level of the water table of about 1.5 m/year, on average, has been observed during the last five years. Farmers are thus called upon to improve irrigation methods. Thus, localized or drip irrigation is adopted to allow rational use of water. The importance of this irrigation system is due to the fact that water is applied directly to the root zone and its compatibility with fertilization. However, this irrigation system faces a thorny problem which is the clogging of pipes and drippers. This leads to a lack of uniformity of irrigation over time. This so-called scaling phenomenon, the consequences of which are harmful (cleaning or replacement of pipes), leads to considerable unproductive expenditure. The objective set by this work is the search for green inhibitors likely to prevent this phenomenon of scaling. This study requires a better knowledge of these waters, their physico-chemical characteristics and their scaling power. Thus, using the "LCGE" controlled degassing technique, we initially evaluated, on pure calco-carbonic water at 30°F, the scaling-inhibiting power of some available plant extracts in our region of Souss-Massa. We then carried out a comparative study of the efficacy of these green inhibitors. The action of the most effective green inhibitor on real agricultural waters was then studied.

Keywords: green inhibitors, localized irrigation, plant extracts, scaling inhibition

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Izadi Banafsheh, Sedaghat Reza

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Maintenance, Repair, and Overhaul (MRO) of machinery are a key recent issue concerning the automotive industry. It has always been a debated question what order or priority should be adopted for the MRO of machinery. This study attempts to examine several criteria including process sensitivity, average time between machine failures, average duration of repair, availability of parts, availability of maintenance personnel and workload through a literature review and experts survey so as to determine the condition of the machine. According to the mentioned criteria, the machinery were ranked in four modes below: A) Need for inspection, B) Need for minor repair, C) Need for part replacement, and D) Need for major repair. The Fuzzy AHP was employed to determine the weighting of criteria. At the end, the obtained weights were ranked through the AHP for each criterion, three groups were specified: shaving machines, assembly and painting in four modes. The statistical population comprises the elite in the Iranian automotive industry at IKCO covering operation managers, CEOs and maintenance professionals who are highly specialized in MRO and perfectly knowledgeable in how the machinery function. The information required for this study were collected from both desk research and field review, which eventually led to construction of a questionnaire handed out to the sample respondents in order to collect information on the subject matter. The results of the AHP for weighting the criteria revealed that the availability of maintenance personnel was the top priority at coefficient of 0.206, while the process sensitivity took the last priority at coefficient of 0.066. Furthermore, the results of TOPSIS for prioritizing the IKCO machinery suggested that at the mode where there is need for inspection, the assembly machines took the top priority while paining machines took the third priority. As for the mode where there is need for minor repairs, the assembly machines took the top priority while the third priority belonged to the shaving machines. As for the mode where there is need for parts replacement, the assembly machines took the top priority while the third belonged to the paining machinery. Finally, as for the mode where there is need for major repair, the assembly machines took the top priority while the third belonged to the paining machinery.

Keywords: maintenance, repair, overhaul, MRO, prioritization of machinery, fuzzy logic, AHP, TOPSIS

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Authors: Malihe Moeinin, Robert Hill, Ferranti Wong

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Background: A white spot lesion (WSL) is defined as subsurface enamel porosity from carious demineralisation on the smooth surfaces of the tooth. It appears as a milky white opacity. Lesions shown an apparently intact surface layer, followed underneath by the more porous lesion body. The small pores within the body of the lesion act as diffusion pathway for both acids and minerals, so allowing the demineralisation of enamel to occur at the advancing front of the lesion. Objectives: The objective is to mapthe porosity and its size on WSL with Focused Ion Bean- Scanning Electron Microscopy (FIB-SEM) Method: The basic method used for FIB-SEM consisted of depositing a one micron thick layer of platinum over 25μmx 25μm of the interest region of enamel. Then, making a rough cut (25μmx 5μmx 20μm) with 3nA current and 30Kv was applied with the help of drift suppression (DS), using a standard “cross-sectional” cutting pattern, which ended at the front of the deposited platinum layer. Two adjacent areas (25μmx 5μmx 20μm) on the both sides of the platinum layer were milled under the same conditions. Subsequent, cleaning cross-sections were applied to polish the sub-surface edge of interest running perpendicular to the surface. The "slice and view" was carried out overnight for milling almost 700 slices with 2Kv and 4nA and taking backscattered (BS) images. Then, images were imported into imageJ and analysed. Results: The prism structure is clearly apparent on FIB-SEM slices of WSL with the dissolution of prism boundaries as well as internal porosity within the prism itself. Porosity scales roughly 100-400nm, which is comparable to the light wavelength (500nm). Conclusion: FIB-SEM is useful to characterize the porosity of WSL and it clearly shows the difference between WSL and normal enamel.

Keywords: white spot lesion, FIB-SEM, enamel porosity, porosity

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Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

Procedia PDF Downloads 146

Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

Procedia PDF Downloads 73

Authors: Geeta Gahlawat, Ashok Kumar Srivastava

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In the present study, a simple drain and fill protocol strategy of repeated batch was adopted for enhancement in polyhydroxyalkanoates (PHAs) production using alcaligenes latus DSM 1124. Repeated batch strategy helped in increasing the longevity of otherwise decaying culture in the bioreactor by supplementing fresh substrates during each cycle of repeated-batch. The main advantages of repeated batch are its ease of operation, enhancement of culture stability towards contamination, minimization of pre-culture effects and maintenance of organism at high growth rates. The cultivation of A. latus was carried out in 7 L bioreactor containing 4 L optimized nutrient medium and a comparison with the batch mode fermentation was done to evaluate the performance of repeated batch in terms of PHAs accumulation and productivity. The statistically optimized medium recipe consisted of: 25 g/L Sucrose, 2.8 g/L (NH4)2SO4, 3.25 g/L KH2PO4, 3.25 g/L Na2HPO4, 0.2 g/L MgSO4, 1.5 mL/L trace element solution. In this strategy, 20% (v/v) of the culture broth was removed from the reactor and supplemented with an equal volume of fresh medium when sucrose concentration inside the reactor decreased below 8 g/L. The fermenter was operated for three repeated batch cycles and fresh nutrient feeding was done at 27 h, 48 h, and 60 h. Repeated batch operation resulted in a total biomass of 27.89 g/L and PHAs concentration 20.55 g/L at the end of 69 h which was a marked improvement as compared to batch cultivation (8.71 g/L biomass and 6.24 g/L PHAs). This strategy demonstrated 3.3 fold and 1.8 fold increase in PHAs concentration and volumetric productivity, respectively as compared to batch cultivation. Repeated batch cultivation strategy had also the benefit of avoiding non-productive time period required for cleaning, refilling and sterilization of bioreactor, thereby increasing the overall volumetric productivity and making the entire process cost-effective too.

Keywords: alcaligenes, biodegradation, polyhydroxyalkanoates, repeated batch

Procedia PDF Downloads 363

Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Jigg Pelayo, Ricardo Villar

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The Philippines is long considered as a valuable producer of high value crops globally. The country’s employment and economy have been dependent on agriculture, thus increasing its demand for the efficient agricultural mechanism. Remote sensing and geographic information technology have proven to effectively provide applications for precision agriculture through image-processing technique considering the development of the aerial scanning technology in the country. Accurate information concerning the spatial correlation within the field is very important for precision farming of high value crops, especially. The availability of height information and high spatial resolution images obtained from aerial scanning together with the development of new image analysis methods are offering relevant influence to precision agriculture techniques and applications. In this study, an algorithm was developed and implemented to detect and count high value crops simultaneously through adaptive scaling of support vector machine (SVM) algorithm subjected to object-oriented approach combining watershed transformation and local maxima filter in enhancing tree counting and detection. The methodology is compared to cutting-edge template matching algorithm procedures to demonstrate its effectiveness on a demanding tree is counting recognition and delineation problem. Since common data and image processing techniques are utilized, thus can be easily implemented in production processes to cover large agricultural areas. The algorithm is tested on high value crops like Palm, Mango and Coconut located in Misamis Oriental, Philippines - showing a good performance in particular for young adult and adult trees, significantly 90% above. The s inventories or database updating, allowing for the reduction of field work and manual interpretation tasks.

Keywords: high value crop, LiDAR, OBIA, precision agriculture

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Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Amal Machtalay

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In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

Procedia PDF Downloads 108

Authors: A. Boualouch, A. Frigui, T. Nasser, A. Essadki, A.Boukhriss

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This work deals with the vector control of the active and reactive powers of a Double-Fed Induction generator DFIG used as a wind generator by the polynomial RST controller. The control of the statoric power transfer between the machine and the grid is achieved by acting on the rotor parameters and control is provided by the polynomial controller RST. The performance and robustness of the controller are compared with PI controller and evaluated by simulation results in MATLAB/simulink.

Keywords: DFIG, RST, vector control, wind turbine

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Authors: Ana Belon Hercilla

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The study is developed in “Reactivos Jeans” company, in the city of Arequipa, whose main business is the laundry of garments at an industrial level. In 2012 the company initiated actions to provide a dry cleaning service of alpaca fiber garments, recognizing that this item is in a growth phase in Peru. Additionally this company took the initiative to use a new greenwashing technology which has not yet been developed in the country. To accomplish this, a redesign of both the process and the plant layout was required. For redesigning the plant, the methodology used was the Systemic Layout Planning, allowing this study divided into four stages. First stage is the information gathering and evaluation of the initial situation of the company, for which a description of the areas, facilities and initial equipment, distribution of the plant, the production process and flows of major operations was made. Second stage is the development of engineering techniques that allow the logging and analysis procedures, such as: Flow Diagram, Route Diagram, DOP (process flowchart), DAP (analysis diagram). Then the planning of the general distribution is carried out. At this stage, proximity factors of the areas are established, the Diagram Paths (TRA) is developed, and the Relational Diagram Activities (DRA). In order to obtain the General Grouping Diagram (DGC), further information is complemented by a time study and Guerchet method is used to calculate the space requirements for each area. Finally, the plant layout redesigning is presented and the implementation of the improvement is made, making it possible to obtain a model much more efficient than the initial design. The results indicate that the implementation of the new machinery, the adequacy of the plant facilities and equipment relocation resulted in a reduction of the production cycle time by 75.67%, routes were reduced by 68.88%, the number of activities during the process were reduced by 40%, waits and storage were removed 100%.

Keywords: redesign, time optimization, industrial laundry, greenwashing

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Authors: Robyn Augustine, Irena Petrinic, Claus Helix-Nielsen, Marshall S. Sheldon

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This study examined the performance of two commercial forward osmosis (FO) membranes; an aquaporin (AQP) based biomimetic membrane, and cellulose triacetate (CTA) membrane in a fertiliser is drawn forward osmosis (FDFO) system for the reclamation of water from synthetic winery wastewater (SWW) operated over 24 hr. Straight, 1 M KCl and 1 M NH₄NO₃ fertiliser solutions were evaluated as draw solutions in the FDFO system. The performance of the AQP-based biomimetic and CTA FO membranes were evaluated in terms of permeate water flux (Jw), reverse solute flux (Js) and percentage water recovery (Re). The average water flux and reverse solute flux when using 1 M KCl as a draw solution against controlled feed solution, deionised (DI) water, was 11.65 L/m²h and 3.98 g/m²h (AQP) and 6.24 L/m²h and 2.89 g/m²h (CTA), respectively. Using 1 M NH₄NO₃ as a draw solution yielded average water fluxes and reverse solute fluxes of 10.73 L/m²h and 1.31 g/m²h (AQP) and 5.84 L/m²h and 1.39 g/m²h (CTA), respectively. When using SWW as the feed solution and 1 M KCl and 1 M NH₄NO₃ as draw solutions, respectively, the average water fluxes observed were 8.15 and 9.66 L/m²h (AQP) and 5.02 and 5.65 L/m²h (CTA). Membrane water flux decline was the result of a combined decrease in the effective driving force of the FDFO system, reverse solute flux and organic fouling. Permeate water flux recoveries of between 84-98%, and 83-89% were observed for the AQP-based biomimetic and CTA membrane, respectively after physical cleaning by flushing was employed. The highest water recovery rate of 49% was observed for the 1 M KCl fertiliser draw solution with AQP-based biomimetic membrane and proved superior in the reclamation of water from SWW.

Keywords: aquaporin biomimetic membrane, cellulose triacetate membrane, forward osmosis, reverse solute flux, synthetic winery wastewater and water flux

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Authors: Narayana Murthy Akurathi, Vijaya Lakshmi Marella

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The literature is reviewed with regard to online Super critical fluid extraction (SFE) coupled directly with supercritical fluid chromatography (SFC) -mass spectrometry that have typically more sensitive than conventional LC-MS/MS and GC-MS/MS. It is becoming increasingly interesting to use on-line techniques that combine sample preparation, separation and detection in one analytical set up. This provides less human intervention, uses small amount of sample and organic solvent and yields enhanced analyte enrichment in a shorter time. The sample extraction is performed under light shielding and anaerobic conditions, preventing the degradation of thermo labile analytes. It may be able to analyze compounds over a wide polarity range as SFC generally uses carbon dioxide which was collected as a by-product of other chemical reactions or is collected from the atmosphere as it contributes no new chemicals to the environment. The diffusion of solutes in supercritical fluids is about ten times greater than that in liquids and about three times less than in gases which results in a decrease in resistance to mass transfer in the column and allows for fast high resolution separations. The drawback of SFC when using carbon dioxide as mobile phase is that the direct introduction of water samples poses a series of problems, water must therefore be eliminated before it reaches the analytical column. Hundreds of compounds analysed simultaneously by simple enclosing in an extraction vessel. This is mainly applicable for pharmaceutical industry where it can analyse fatty acids and phospholipids that have many analogues as their UV spectrum is very similar, trace additives in polymers, cleaning validation can be conducted by putting swab sample in an extraction vessel, analysing hundreds of pesticides with good resolution.

Keywords: super critical fluid extraction (SFE), super critical fluid chromatography (SFC), LCMS/MS, GCMS/MS

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Authors: Tommy Krieger, Klaus Gruendler

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We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its flexibility it is also a valuable tool for comparison studies.

Keywords: democracy, democracy index, machine learning, support vector machines

Procedia PDF Downloads 368

Authors: Yichao Liu, Peter Fransson, Julian Heidecke, Jonas Wallin, Joacim Rockloev

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Dengue, a mosquito-borne viral disease, poses a significant public health challenge in endemic tropical or subtropical countries, including Sri Lanka. To reveal insights into the complexity of the dynamics of this disease and study the drivers, a comprehensive model capable of both robust forecasting and insightful inference of drivers while capturing the co-circulating of several virus strains is essential. However, existing studies mostly focus on only one aspect at a time and do not integrate and carry insights across the siloed approach. While mechanistic models are developed to capture immunity dynamics, they are often oversimplified and lack integration of all the diverse drivers of disease transmission. On the other hand, purely data-driven methods lack constraints imposed by immuno-epidemiological processes, making them prone to overfitting and inference bias. This research presents a hybrid model that combines machine learning techniques with mechanistic modelling to overcome the limitations of existing approaches. Leveraging eight years of newly reported dengue case data, along with socioeconomic factors, such as human mobility, weekly climate data from 2011 to 2018, genetic data detecting the introduction and presence of new strains, and estimates of seropositivity for different districts in Sri Lanka, we derive a data-driven vector (SEI) to human (SEIR) model across 16 regions in Sri Lanka at the weekly time scale. By conducting ablation studies, the lag effects allowing delays up to 12 weeks of time-varying climate factors were determined. The model demonstrates superior predictive performance over a pure machine learning approach when considering lead times of 5 and 10 weeks on data withheld from model fitting. It further reveals several interesting interpretable findings of drivers while adjusting for the dynamics and influences of immunity and introduction of a new strain. The study uncovers strong influences of socioeconomic variables: population density, mobility, household income and rural vs. urban population. The study reveals substantial sensitivity to the diurnal temperature range and precipitation, while mean temperature and humidity appear less important in the study location. Additionally, the model indicated sensitivity to vegetation index, both max and average. Predictions on testing data reveal high model accuracy. Overall, this study advances the knowledge of dengue transmission in Sri Lanka and demonstrates the importance of incorporating hybrid modelling techniques to use biologically informed model structures with flexible data-driven estimates of model parameters. The findings show the potential to both inference of drivers in situations of complex disease dynamics and robust forecasting models.

Keywords: compartmental model, climate, dengue, machine learning, social-economic

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Authors: Asma Ameur, Dhafer Malouche

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Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique.

Keywords: clustering, consumer behavior, data mining, e-reputation, machine learning, neural network, online hotel ‘reviews, opinion mining, scoring

Procedia PDF Downloads 129

Authors: L. Bammou, M. Belkhaouda R. Salghi, L. Bazzi, B. Hammouti

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Steel and steel-based alloys of different grades steel are extensively used in numerous applications where acid solutions are widely applied such as industrial acid pickling, industrial acid cleaning and oil-well acidizing. The use of chemical inhibitors is one of the most practical methods for the protection against corrosion in acidic media. Most of the excellent acid inhibitors are organic compounds containing nitrogen, oxygen, phosphorus and sulphur. The use of non-toxic inhibitors called green or eco-friendly environmental inhibitors is one of the solutions possible to prevent the corrosion of the material. These advantages have incited us to draw a large part of program of our laboratory to examine natural substances as corrosion inhibitors such as: prickly pear seed oil, Argan oil, Argan extract, Fennel oil, Rosemary oil, Thymus oil, Lavender oil, Jojoba oil, Pennyroyal Mint oil, and Artemisia. In the present work, we investigate the corrosion inhibition of steel in 1 M HCl by junipers extract using weight loss, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) methods. The result obtained of junipers extract (JE) shows excellent inhibition properties for the corrosion of C38 steel in 1M HCl at 298K, and the inhibition efficiency increases with increasing of the JE concentration. The inhibitor efficiencies determined by weight loss, Tafel polarisation and EIS methods are in reasonable agreement. Based on the polarisation results, the investigated junipers extract can be classified as mixed inhibitor. The calculated structural parameters show increase of the obtained Rct values and decrease of the capacitance, Cdl, with JE concentration increase. It is suggested to attribute this to the increase of the thickness of the adsorption layer at steel surface. The adsorption model obeys to the Langmuir adsorption isotherm. The adsorption process is a spontaneous and exothermic process.

Keywords: corrosion inhibition, steel, friendly inhibitors, Tafel polarisation

Procedia PDF Downloads 512

Authors: L. Piñeiro, N. Cobas, L. Gómez-Limia, S. Martínez, I. Franco

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The swordfish (Xiphias gladius) is a large oceanic fish of high commercial value, which is widely distributed in waters of the world’s oceans. They are considered to be an important source of high quality proteins, vitamins and essential fatty acids, although only half of the population follows the recommendation of nutritionists to consume fish at least twice a week. Swordfish is consumed worldwide because of its low fat content and high protein content. It is generally sold as fresh, frozen, and as pieces or slices. The aim of this study was to evaluate the effect of salting and frying on the composition of the water-soluble vitamins (B₂, B₃, B₉ and B₁₂) and fat-soluble vitamins (A, D, and E) of swordfish. Three loins of swordfish from Pacific Ocean were analyzed. All the fishes had a weight between 50 and 70 kg and were transported to the laboratory frozen (-18 ºC). Before the processing, they were defrosted at 4 ºC. Each loin was sliced and salted in brine. After cleaning the slices, they were divided into portions (10×2 cm) and fried in olive oil. The identification and quantification of vitamins were carried out by high-performance liquid chromatography (HPLC), using methanol and 0.010% trifluoroacetic acid as mobile phases at a flow-rate of 0.7 mL min^-1. The UV-Vis detector was used for the detection of the water- and fat-soluble vitamins (A and D), as well as the fluorescence detector for the detection of the vitamin E. During salting, water and fat-soluble vitamin contents remained constant, observing an evident decrease in the values of vitamin B₂. The diffusion of salt into the interior of the pieces and the loss of constitution water that occur during this stage would be related to this significant decrease. In general, after frying water-soluble and fat-soluble vitamins showed a great thermolability with high percentages of retention with values among 50–100%. Vitamin B₃ is the one that exhibited higher percentages of retention with values close to 100%. However, vitamin B₉ presented the highest losses with a percentage of retention of less than 20%.

Keywords: frying, HPLC, salting, swordfish, vitamins

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Authors: John Goodhue, Xiaonan Wei

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Current legal decisions and policies regarding the naming as artificial intelligence as inventor are reviewed with emphasis on the recent decisions by the European Patent Office regarding the DABUS inventions holding that an artificial intelligence machine cannot be an inventor. Next, a set of hypotheticals is introduced and examined to better understand how artificial intelligence might be used to create or assist in creating new inventions and how application of existing or proposed changes in the law would affect the ability to protect these inventions including due to restrictions on artificial intelligence for being named as inventors, ownership of inventions made by artificial intelligence, and the effects on legal standards for inventiveness or obviousness.

Keywords: Artificial intelligence, innovation, invention, patent

Procedia PDF Downloads 168

Authors: Rahou Mohamed , Sebaa Fethi, Cheikh Abdelmadjid

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Manufacturing tolerancing is intended to determine the intermediate geometrical and dimensional states of the part during its manufacturing process. These manufacturing dimensions also serve to satisfy not only the functional requirements given in the definition drawing but also the manufacturing constraints, for example geometrical defects of the machine, vibration, and the wear of the cutting tool. The choice of positioning has an important influence on the cost and quality of manufacture. To avoid this problem, a two-step approach have been developed. The first step is dedicated to the determination of the optimum position. As for the second step, a study was carried out for the tightening effect on the tolerance interval.

Keywords: dispersion, tolerance, manufacturing, position

Procedia PDF Downloads 330

Authors: Saadia Elamin

Abstract:

The quasi-universal use of smart phones with internet connection available all the time makes it a reflex action for translation undergraduates, once they encounter the least translation problem, to turn to the freely available web resource: Google Translate. Like for other translator resources and aids, the use of Google Translate needs to be moderated in such a way that it contributes to developing translation competence. Here, instead of interfering with students’ learning by providing ready-made solutions which might not always fit into the contexts of use, it can help to consolidate the skills of analysis and transfer which students have already acquired. One way to do so is by training students to adhere to the basic principles of translation work. The most important of these is that analyzing the source text for comprehension comes first and foremost before jumping into the search for target language equivalents. Another basic principle is that certain translator aids and tools can be used for comprehension, while others are to be confined to the phase of re-expressing the meaning into the target language. The present paper reports on the experience of making a measured and reasonable use of Google Translate in translation teaching at Prince Sultan University (PSU), Riyadh. First, it traces the development that has taken place in the field of translation in this age of information technology, be it in translation teaching and translator training, or in the real-world practice of the profession. Second, it describes how, with the aim of reflecting this development onto the way translation is taught, senior students, after being trained on post-editing machine translation output, are authorized to use Google Translate in classwork and assignments. Third, the paper elaborates on the findings of this case study which has demonstrated that Google Translate, if used at the appropriate levels of training, can help to enhance students’ ability to perform different translation tasks. This help extends from the search for terms and expressions, to the tasks of drafting the target text, revising its content and finally editing it. In addition, using Google Translate in this way fosters a reflexive and critical attitude towards web resources in general, maximizing thus the benefit gained from them in preparing students to meet the requirements of the modern translation job market.

Keywords: Google Translate, post-editing machine translation output, principles of translation work, translation competence, translation teaching, translator aids and tools

Procedia PDF Downloads 466